{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "create_sine_model.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sblS7n3zWCWV",
        "colab_type": "text"
      },
      "source": [
        "**Copyright 2019 The TensorFlow Authors.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rvUzWmoWMH5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aCZBFzjClURz",
        "colab_type": "text"
      },
      "source": [
        "# Create and convert a TensorFlow model\n",
        "This notebook is designed to demonstrate the process of creating a TensorFlow model and converting it to use with TensorFlow Lite. The model created in this notebook is used in the [hello_world](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro/examples/hello_world) sample for [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers/overview).\n",
        "\n",
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/hello_world/create_sine_model.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/hello_world/create_sine_model.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dh4AXGuHWeu1",
        "colab_type": "text"
      },
      "source": [
        "## Import dependencies\n",
        "Our first task is to import the dependencies we need. Run the following cell to do so:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53PBJBv1jEtJ",
        "colab_type": "code",
        "outputId": "9b035753-60e5-43db-a78d-284ea9de9513",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        }
      },
      "source": [
        "# TensorFlow is an open source machine learning library\n",
        "# Note: The following line is temporary to use v2\n",
        "!pip install tensorflow==2.0.0-beta0\n",
        "import tensorflow as tf\n",
        "# Numpy is a math library\n",
        "import numpy as np\n",
        "# Matplotlib is a graphing library\n",
        "import matplotlib.pyplot as plt\n",
        "# math is Python's math library\n",
        "import math"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-PuBEb6CMeo",
        "colab_type": "text"
      },
      "source": [
        "## Generate data\n",
        "Deep learning networks learn to model patterns in underlying data. In this notebook, we're going to train a network to model data generated by a [sine](https://en.wikipedia.org/wiki/Sine) function. This will result in a model that can take a value, `x`, and predict its sine, `y`.\n",
        "\n",
        "In a real world application, if you needed the sine of `x`, you could just calculate it directly. However, by training a model to do this, we can demonstrate the basic principles of machine learning.\n",
        "\n",
        "In the [hello_world](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite/experimental/micro/examples/hello_world) sample for [TensorFlow Lite for Microcontrollers](https://www.tensorflow.org/lite/microcontrollers/overview), we'll use this model to control LEDs that light up in a sequence.\n",
        "\n",
        "The code in the following cell will generate a set of random `x` values, calculate their sine values, and display them on a graph:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKjg7QeMDsDx",
        "colab_type": "code",
        "outputId": "b17a43c6-eba1-4cc7-8807-14fcf5918d01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# We'll generate this many sample datapoints\n",
        "SAMPLES = 1000\n",
        "\n",
        "# Set a \"seed\" value, so we get the same random numbers each time we run this\n",
        "# notebook\n",
        "np.random.seed(1337)\n",
        "\n",
        "# Generate a uniformly distributed set of random numbers in the range from\n",
        "# 0 to 2π, which covers a complete sine wave oscillation\n",
        "x_values = np.random.uniform(low=0, high=2*math.pi, size=SAMPLES)\n",
        "\n",
        "# Shuffle the values to guarantee they're not in order\n",
        "np.random.shuffle(x_values)\n",
        "\n",
        "# Calculate the corresponding sine values\n",
        "y_values = np.sin(x_values)\n",
        "\n",
        "# Plot our data. The 'b.' argument tells the library to print blue dots.\n",
        "plt.plot(x_values, y_values, 'b.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAD8CAYAAABzTgP2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nb+vxVQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iWOlC7W_FYvA",
        "colab_type": "text"
      },
      "source": [
        "## Add some noise\n",
        "Since it was generated directly by the sine function, our data fits a nice, smooth curve.\n",
        "\n",
        "However, machine learning models are good at extracting underlying meaning from messy, real world data. To demonstrate this, we can add some noise to our data to approximate something more life-like.\n",
        "\n",
        "In the following cell, we'll add some random noise to each value, then draw a new graph:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0FJe3Y-Gkac",
        "colab_type": "code",
        "outputId": "60b19cdd-c69c-469e-9446-b738a79c1f51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# Add a small random number to each y value\n",
        "y_values += 0.1 * np.random.randn(*y_values.shape)\n",
        "\n",
        "# Plot our data\n",
        "plt.plot(x_values, y_values, 'b.')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Up8Xk_pMH4Rt",
        "colab_type": "text"
      },
      "source": [
        "## Split our data\n",
        "We now have a noisy dataset that approximates real world data. We'll be using this to train our model.\n",
        "\n",
        "To evaluate the accuracy of the model we train, we'll need to compare its predictions to real data and check how well they match up. This evaluation happens during training (where it is referred to as validation) and after training (referred to as testing) It's important in both cases that we use fresh data that was not already used to train the model.\n",
        "\n",
        "To ensure we have data to use for evaluation, we'll set some aside before we begin training. We'll reserve 20% of our data for validation, and another 20% for testing. The remaining 60% will be used to train the model. This is a typical split used when training models.\n",
        "\n",
        "The following code will split our data and then plot each set as a different color:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNYko5L1keqZ",
        "colab_type": "code",
        "outputId": "b9f9c57b-b6aa-4817-8ab4-4a2201732b9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# We'll use 60% of our data for training and 20% for testing. The remaining 20%\n",
        "# will be used for validation. Calculate the indices of each section.\n",
        "TRAIN_SPLIT =  int(0.6 * SAMPLES)\n",
        "TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)\n",
        "\n",
        "# Use np.split to chop our data into three parts.\n",
        "# The second argument to np.split is an array of indices where the data will be\n",
        "# split. We provide two indices, so the data will be divided into three chunks.\n",
        "x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])\n",
        "y_train, y_test, y_validate = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])\n",
        "\n",
        "# Double check that our splits add up correctly\n",
        "assert (x_train.size + x_validate.size + x_test.size) ==  SAMPLES\n",
        "\n",
        "# Plot the data in each partition in different colors:\n",
        "plt.plot(x_train, y_train, 'b.', label=\"Train\")\n",
        "plt.plot(x_test, y_test, 'r.', label=\"Test\")\n",
        "plt.plot(x_validate, y_validate, 'y.', label=\"Validate\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDFi6aXY5AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t5McVnHmNiDw",
        "colab_type": "text"
      },
      "source": [
        "## Design a model\n",
        "We're going to build a model that will take an input value (in this case, `x`) and use it to predict a numeric output value (the sine of `x`). This type of problem is called a _regression_.\n",
        "\n",
        "To achieve this, we're going to create a simple neural network. It will use _layers_ of _neurons_ to attempt to learn any patterns underlying the training data, so it can make predictions.\n",
        "\n",
        "To begin with, we'll define two layers. The first layer takes a single input (our `x` value) and runs it through 16 neurons. Based on this input, each neuron will become _activated_ to a certain degree based on its internal state (its _weight_ and _bias_ values). A neuron's degree of activation is expressed as a number.\n",
        "\n",
        "The activation numbers from our first layer will be fed as inputs to our second layer, which is a single neuron. It will apply its own weights and bias to these inputs and calculate its own activation, which will be output as our `y` value.\n",
        "\n",
        "**Note:** To learn more about how neural networks function, you can explore the [Learn TensorFlow](https://codelabs.developers.google.com/codelabs/tensorflow-lab1-helloworld) codelabs.\n",
        "\n",
        "The code in the following cell defines our model using [Keras](https://www.tensorflow.org/guide/keras), TensorFlow's high-level API for creating deep learning networks. Once the network is defined, we _compile_ it, specifying parameters that determine how it will be trained:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gD60bE8cXQId",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# We'll use Keras to create a simple model architecture\n",
        "from tensorflow.keras import layers\n",
        "model_1 = tf.keras.Sequential()\n",
        "\n",
        "# First layer takes a scalar input and feeds it through 16 \"neurons\". The\n",
        "# neurons decide whether to activate based on the 'relu' activation function.\n",
        "model_1.add(layers.Dense(16, activation='relu', input_shape=(1,)))\n",
        "\n",
        "# Final layer is a single neuron, since we want to output a single value\n",
        "model_1.add(layers.Dense(1))\n",
        "\n",
        "# Compile the model using a standard optimizer and loss function for regression\n",
        "model_1.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O0idLyRLQeGj",
        "colab_type": "text"
      },
      "source": [
        "## Train the model\n",
        "Once we've defined the model, we can use our data to _train_ it. Training involves passing an `x` value into the neural network, checking how far the network's output deviates from the expected `y` value, and adjusting the neurons' weights and biases so that the output is more likely to be correct the next time.\n",
        "\n",
        "Training runs this process on the full dataset multiple times, and each full run-through is known as an _epoch_. The number of epochs to run during training is a parameter we can set.\n",
        "\n",
        "During each epoch, data is run through the network in multiple _batches_. Each batch, several pieces of data are passed into the network, producing output values. These outputs' correctness is measured in aggregate and the network's weights and biases are adjusted accordingly, once per batch. The _batch size_ is also a parameter we can set.\n",
        "\n",
        "The code in the following cell uses the `x` and `y` values from our training data to train the model. It runs for 1000 _epochs_, with 16 pieces of data in each _batch_. We also pass in some data to use for _validation_. As you will see when you run the cell, training can take a while to complete:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8hQKr4cVOdE",
        "colab_type": "code",
        "outputId": "3f1a7904-ffcd-4bb7-8bbb-bcd85a132128",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Train the model on our training data while validating on our validation set\n",
        "history_1 = model_1.fit(x_train, y_train, epochs=1000, batch_size=16,\n",
        "                    validation_data=(x_validate, y_validate))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 600 samples, validate on 200 samples\n",
            "Epoch 1/1000\n",
            "600/600 [==============================] - 0s 412us/sample - loss: 0.5016 - mae: 0.6297 - val_loss: 0.4922 - val_mae: 0.6235\n",
            "Epoch 2/1000\n",
            "600/600 [==============================] - 0s 105us/sample - loss: 0.3905 - mae: 0.5436 - val_loss: 0.4262 - val_mae: 0.5641\n",
            "...\n",
            "Epoch 998/1000\n",
            "600/600 [==============================] - 0s 109us/sample - loss: 0.1535 - mae: 0.3068 - val_loss: 0.1507 - val_mae: 0.3113\n",
            "Epoch 999/1000\n",
            "600/600 [==============================] - 0s 100us/sample - loss: 0.1545 - mae: 0.3077 - val_loss: 0.1499 - val_mae: 0.3103\n",
            "Epoch 1000/1000\n",
            "600/600 [==============================] - 0s 132us/sample - loss: 0.1530 - mae: 0.3045 - val_loss: 0.1542 - val_mae: 0.3143\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cRE8KpEqVfaS",
        "colab_type": "text"
      },
      "source": [
        "## Check the training metrics\n",
        "During training, the model's performance is constantly being measured against both our training data and the validation data that we set aside earlier. Training produces a log of data that tells us how the model's performance changed over the course of the training process.\n",
        "\n",
        "The following cells will display some of that data in a graphical form:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmvA-ksoln8r",
        "colab_type": "code",
        "outputId": "1b834831-81e8-4548-dd8c-f5edf2c3ff43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Draw a graph of the loss, which is the distance between\n",
        "# the predicted and actual values during training and validation.\n",
        "loss = history_1.history['loss']\n",
        "val_loss = history_1.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWBhjjAno/wGVkooxFkdVNgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iOFBSbPcYCN4",
        "colab_type": "text"
      },
      "source": [
        "## Look closer at the data\n",
        "The graph shows the _loss_ (or the difference between the model's predictions and the actual data) for each epoch. There are several ways to calculate loss, and the method we have used is _mean squared error_. There is a distinct loss value given for the training and the validation data.\n",
        "\n",
        "As we can see, the amount of loss rapidly decreases over the first 25 epochs, before flattening out. This means that the model is improving and producing more accurate predictions!\n",
        "\n",
        "Our goal is to stop training when either the model is no longer improving, or when the _training loss_ is less than the _validation loss_, which would mean that the model has learned to predict the training data so well that it can no longer generalize to new data.\n",
        "\n",
        "To make the flatter part of the graph more readable, let's skip the first 50 epochs:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zo0RYroFZYIV",
        "colab_type": "code",
        "outputId": "e6841332-0541-44bb-a186-ae5b46781e51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Exclude the first few epochs so the graph is easier to read\n",
        "SKIP = 50\n",
        "\n",
        "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n",
        "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4EQD-Bb8hLM",
        "colab_type": "text"
      },
      "source": [
        "## Further metrics\n",
        "From the plot, we can see that loss continues to reduce until around 600 epochs, at which point it is mostly stable. This means that there's no need to train our network beyond 600 epochs.\n",
        "\n",
        "However, we can also see that the lowest loss value is still around 0.155. This means that our network's predictions are off by an average of ~15%. In addition, the validation loss values jump around a lot, and is sometimes even higher.\n",
        "\n",
        "To gain more insight into our model's performance we can plot some more data. This time, we'll plot the _mean absolute error_, which is another way of measuring how far the network's predictions are from the actual numbers:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Md9E_azmpkZU",
        "colab_type": "code",
        "outputId": "39b97561-b01d-49f2-c35c-fbd8db663806",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.clf()\n",
        "\n",
        "# Draw a graph of mean absolute error, which is another way of\n",
        "# measuring the amount of error in the prediction.\n",
        "mae = history_1.history['mae']\n",
        "val_mae = history_1.history['val_mae']\n",
        "\n",
        "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n",
        "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n",
        "plt.title('Training and validation mean absolute error')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('MAE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ctawd0CXAVEw",
        "colab_type": "text"
      },
      "source": [
        "This graph of _mean absolute error_ tells another story. We can see that training data shows consistently lower error than validation data, which means that the network may have _overfit_, or learned the training data so rigidly that it can't make effective predictions about new data.\n",
        "\n",
        "In addition, the mean absolute error values are quite high, ~0.305 at best, which means some of the model's predictions are at least 30% off. A 30% error means we are very far from accurately modelling the sine wave function.\n",
        "\n",
        "To get more insight into what is happening, we can plot our network's predictions for the training data against the expected values:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i13eVIT3B9Mj",
        "colab_type": "code",
        "outputId": "afc103e2-0beb-4a26-fe18-c0cccc6d3d2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# Use the model to make predictions from our validation data\n",
        "predictions = model_1.predict(x_train)\n",
        "\n",
        "# Plot the predictions along with to the test data\n",
        "plt.clf()\n",
        "plt.title('Training data predicted vs actual values')\n",
        "plt.plot(x_test, y_test, 'b.', label='Actual')\n",
        "plt.plot(x_train, predictions, 'r.', label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEICAYAAAC3Y/QeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/oZhGGXI/w++6U8tCYD1ygAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wokallj1D21L",
        "colab_type": "text"
      },
      "source": [
        "Oh dear! The graph makes it clear that our network has learned to approximate the sine function in a very limited way. From `0 <= x <= 1.1` the line mostly fits, but for the rest of our `x` values it is a rough approximation at best.\n",
        "\n",
        "The rigidity of this fit suggests that the model does not have enough capacity to learn the full complexity of the sine wave function, so it's only able to approximate it in an overly simplistic way. By making our model bigger, we should be able to improve its performance.\n",
        "\n",
        "## Change our model\n",
        "To make our model bigger, let's add an additional layer of neurons. The following cell redefines our model in the same way as earlier, but with an additional layer of 16 neurons in the middle:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oW0xus6AF-4o",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_2 = tf.keras.Sequential()\n",
        "\n",
        "# First layer takes a scalar input and feeds it through 16 \"neurons\". The\n",
        "# neurons decide whether to activate based on the 'relu' activation function.\n",
        "model_2.add(layers.Dense(16, activation='relu', input_shape=(1,)))\n",
        "\n",
        "# The new second layer may help the network learn more complex representations\n",
        "model_2.add(layers.Dense(16, activation='relu'))\n",
        "\n",
        "# Final layer is a single neuron, since we want to output a single value\n",
        "model_2.add(layers.Dense(1))\n",
        "\n",
        "# Compile the model using a standard optimizer and loss function for regression\n",
        "model_2.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dv2SC409Grap",
        "colab_type": "text"
      },
      "source": [
        "We'll now train the new model. To save time, we'll train for only 600 epochs:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPAUrdkmGq1M",
        "colab_type": "code",
        "outputId": "34ad91e0-229b-479c-bd65-12ad1ed1c660",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "history_2 = model_2.fit(x_train, y_train, epochs=600, batch_size=16,\n",
        "                    validation_data=(x_validate, y_validate))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 600 samples, validate on 200 samples\n",
            "Epoch 1/600\n",
            "600/600 [==============================] - 0s 422us/sample - loss: 0.5655 - mae: 0.6259 - val_loss: 0.4104 - val_mae: 0.5509\n",
            "Epoch 2/600\n",
            "600/600 [==============================] - 0s 111us/sample - loss: 0.3195 - mae: 0.4902 - val_loss: 0.3341 - val_mae: 0.4927\n",
            "...\n",
            "Epoch 598/600\n",
            "600/600 [==============================] - 0s 116us/sample - loss: 0.0124 - mae: 0.0886 - val_loss: 0.0096 - val_mae: 0.0771\n",
            "Epoch 599/600\n",
            "600/600 [==============================] - 0s 130us/sample - loss: 0.0125 - mae: 0.0900 - val_loss: 0.0107 - val_mae: 0.0824\n",
            "Epoch 600/600\n",
            "600/600 [==============================] - 0s 109us/sample - loss: 0.0124 - mae: 0.0892 - val_loss: 0.0116 - val_mae: 0.0845\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mc_CQu2_IvOP",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate our new model\n",
        "Each training epoch, the model prints out its loss and mean absolute error for training and validation. You can read this in the output above (note that your exact numbers may differ): \n",
        "\n",
        "```\n",
        "Epoch 600/600\n",
        "600/600 [==============================] - 0s 109us/sample - loss: 0.0124 - mae: 0.0892 - val_loss: 0.0116 - val_mae: 0.0845\n",
        "```\n",
        "\n",
        "You can see that we've already got a huge improvement - validation loss has dropped from 0.15 to 0.015, and validation MAE has dropped from 0.31 to 0.1.\n",
        "\n",
        "The following cell will print the same graphs we used to evaluate our original model, but showing our new training history:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SYHGswAJJgrC",
        "colab_type": "code",
        "outputId": "efcc51f6-f1f1-490a-ffba-ed283586f83e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 851
        }
      },
      "source": [
        "# Draw a graph of the loss, which is the distance between\n",
        "# the predicted and actual values during training and validation.\n",
        "loss = history_2.history['loss']\n",
        "val_loss = history_2.history['val_loss']\n",
        "\n",
        "epochs = range(1, len(loss) + 1)\n",
        "\n",
        "plt.plot(epochs, loss, 'g.', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Exclude the first few epochs so the graph is easier to read\n",
        "SKIP = 100\n",
        "\n",
        "plt.clf()\n",
        "\n",
        "plt.plot(epochs[SKIP:], loss[SKIP:], 'g.', label='Training loss')\n",
        "plt.plot(epochs[SKIP:], val_loss[SKIP:], 'b.', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "plt.clf()\n",
        "\n",
        "# Draw a graph of mean absolute error, which is another way of\n",
        "# measuring the amount of error in the prediction.\n",
        "mae = history_2.history['mae']\n",
        "val_mae = history_2.history['val_mae']\n",
        "\n",
        "plt.plot(epochs[SKIP:], mae[SKIP:], 'g.', label='Training MAE')\n",
        "plt.plot(epochs[SKIP:], val_mae[SKIP:], 'b.', label='Validation MAE')\n",
        "plt.title('Training and validation mean absolute error')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('MAE')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMMYY4/H/AZN6yxQ6gTLNAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f86dWOyZKmN9",
        "colab_type": "text"
      },
      "source": [
        "Great results! From these graphs, we can see several exciting things:\n",
        "\n",
        "*   Our network has reached its peak accuracy much more quickly (within 200 epochs instead of 600)\n",
        "*   The overall loss and MAE are much better than our previous network\n",
        "*   Metrics are better for validation than training, which means the network is not overfitting\n",
        "\n",
        "The reason the metrics for validation are better than those for training is that validation metrics are calculated at the end of each epoch, while training metrics are calculated throughout the epoch, so validation happens on a model that has been trained slightly longer.\n",
        "\n",
        "This all means our network seems to be performing well! To confirm, let's check its predictions against the test dataset we set aside earlier:\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZfztKKyhLxX",
        "colab_type": "code",
        "outputId": "b792a12e-713d-4b07-9f8e-de0d059d5cdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        }
      },
      "source": [
        "# Calculate and print the loss on our test dataset\n",
        "loss = model_2.evaluate(x_test, y_test)\n",
        "\n",
        "# Make predictions based on our test dataset\n",
        "predictions = model_2.predict(x_test)\n",
        "\n",
        "# Graph the predictions against the actual values\n",
        "plt.clf()\n",
        "plt.title('Comparison of predictions and actual values')\n",
        "plt.plot(x_test, y_test, 'b.', label='Actual')\n",
        "plt.plot(x_test, predictions, 'r.', label='Predicted')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200/200 [==============================] - 0s 146us/sample - loss: 0.0124 - mae: 0.0907\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEICAYAAAC3Y/QeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVxRFKUP+P5OxXtvr2werAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3h7IcvuOOS4J",
        "colab_type": "text"
      },
      "source": [
        "Much better! The evaluation metrics we printed show that the model has a low loss and MAE on the test data, and the predictions line up visually with our data fairly well.\n",
        "\n",
        "The model isn't perfect; its predictions don't form a smooth sine curve. For instance, the line is almost straight when `x` is between 4.2 and 5.2. If we wanted to go further, we could try further increasing the capacity of the model, perhaps using some techniques to defend from overfitting.\n",
        "\n",
        "However, an important part of machine learning is knowing when to quit, and this model is good enough for our use case - which is to make some LEDs blink in a pleasing pattern.\n",
        "\n",
        "## Convert to TensorFlow Lite\n",
        "We now have an acceptably accurate model in-memory. However, to use this with TensorFlow Lite for Microcontrollers, we'll need to convert it into the correct format and download it as a file. To do this, we'll use the [TensorFlow Lite Converter](https://www.tensorflow.org/lite/convert). The converter outputs a file in a special, space-efficient format for use on memory-constrained devices.\n",
        "\n",
        "Since this model is going to be deployed on a microcontroller, we want it to be as tiny as possible! One technique for reducing the size of models is called [quantization](https://www.tensorflow.org/lite/performance/post_training_quantization). It reduces the precision of the model's weights, which saves memory, often without much impact on accuracy. Quantized models also run faster, since the calculations required are simpler.\n",
        "\n",
        "The TensorFlow Lite Converter can apply quantization while it converts the model. In the following cell, we'll convert the model twice: once with quantization, once without:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1muAoUm8lSXL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert the model to the TensorFlow Lite format without quantization\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model_2)\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model to disk\n",
        "open(\"sine_model.tflite\", \"wb\").write(tflite_model)\n",
        "\n",
        "# Convert the model to the TensorFlow Lite format with quantization\n",
        "converter = tf.lite.TFLiteConverter.from_keras_model(model_2)\n",
        "converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]\n",
        "tflite_model = converter.convert()\n",
        "\n",
        "# Save the model to disk\n",
        "open(\"sine_model_quantized.tflite\", \"wb\").write(tflite_model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L_vE-ZDkHVxe",
        "colab_type": "text"
      },
      "source": [
        "## Test the converted models\n",
        "To prove these models are still accurate after conversion and quantization, we'll use both of them to make predictions and compare these against our test results:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-J7IKlXiYVPz",
        "colab_type": "code",
        "outputId": "0c10f56c-dbd7-4cc3-e332-30ad673769e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        }
      },
      "source": [
        "# Instantiate an interpreter for each model\n",
        "sine_model = tf.lite.Interpreter('sine_model.tflite')\n",
        "sine_model_quantized = tf.lite.Interpreter('sine_model_quantized.tflite')\n",
        "\n",
        "# Allocate memory for each model\n",
        "sine_model.allocate_tensors()\n",
        "sine_model_quantized.allocate_tensors()\n",
        "\n",
        "# Get the input and output tensors so we can feed in values and get the results\n",
        "sine_model_input = sine_model.tensor(sine_model.get_input_details()[0][\"index\"])\n",
        "sine_model_output = sine_model.tensor(sine_model.get_output_details()[0][\"index\"])\n",
        "sine_model_quantized_input = sine_model_quantized.tensor(sine_model_quantized.get_input_details()[0][\"index\"])\n",
        "sine_model_quantized_output = sine_model_quantized.tensor(sine_model_quantized.get_output_details()[0][\"index\"])\n",
        "\n",
        "# Create arrays to store the results\n",
        "sine_model_predictions = np.empty(x_test.size)\n",
        "sine_model_quantized_predictions = np.empty(x_test.size)\n",
        "\n",
        "# Run each model's interpreter for each value and store the results in arrays\n",
        "for i in range(x_test.size):\n",
        "  sine_model_input().fill(x_test[i])\n",
        "  sine_model.invoke()\n",
        "  sine_model_predictions[i] = sine_model_output()[0]\n",
        "\n",
        "  sine_model_quantized_input().fill(x_test[i])\n",
        "  sine_model_quantized.invoke()\n",
        "  sine_model_quantized_predictions[i] = sine_model_quantized_output()[0]\n",
        "\n",
        "# See how they line up with the data\n",
        "plt.clf()\n",
        "plt.title('Comparison of various models against actual values')\n",
        "plt.plot(x_test, y_test, 'bo', label='Actual')\n",
        "plt.plot(x_test, predictions, 'ro', label='Original predictions')\n",
        "plt.plot(x_test, sine_model_predictions, 'bx', label='Lite predictions')\n",
        "plt.plot(x_test, sine_model_quantized_predictions, 'gx', label='Lite quantized predictions')\n",
        "plt.legend()\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAEICAYAAAC3Y/QeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlFJx6P8D4Obclx42P3sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jWxvLGexKv0D",
        "colab_type": "text"
      },
      "source": [
        "We can see from the graph that the predictions for the original model, the converted model, and the quantized model are all close enough to be indistinguishable. This means that our quantized model is ready to use!\n",
        "\n",
        "We can print the difference in file size:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6r42iBnULP4X",
        "colab_type": "code",
        "outputId": "afe526c9-498d-498e-d768-1edfbf21e870",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "import os\n",
        "basic_model_size = os.path.getsize(\"sine_model.tflite\")\n",
        "print(\"Basic model is %d bytes\" % basic_model_size)\n",
        "quantized_model_size = os.path.getsize(\"sine_model_quantized.tflite\")\n",
        "print(\"Quantized model is %d bytes\" % quantized_model_size)\n",
        "difference = basic_model_size - quantized_model_size\n",
        "print(\"Difference is %d bytes\" % difference)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Basic model is 2656 bytes\n",
            "Quantized model is 2640 bytes\n",
            "Difference is 16 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C2vpZE9ZshVH",
        "colab_type": "text"
      },
      "source": [
        "Our quantized model is only 16 bytes smaller than the original version, which only a tiny reduction in size! At around 2.6 kilobytes, this model is already so small that the weights make up only a small fraction of the overall size, meaning quantization has little effect.\n",
        "\n",
        "More complex models have many more weights, meaning the space saving from quantization will be much higher, approaching 4x for most sophisticated models.\n",
        "\n",
        "Regardless, our quantized model will take less time to execute than the original version, which is important on a tiny microcontroller!\n",
        "\n",
        "## Write to a C file\n",
        "The final step in preparing our model for use with TensorFlow Lite for Microcontrollers is to convert it into a C source file. You can see an example of this format in [`hello_world/sine_model_data.cc`](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/experimental/micro/examples/hello_world/sine_model_data.cc).\n",
        "\n",
        "To do so, we can use a command line utility named [`xxd`](https://linux.die.net/man/1/xxd). The following cell runs `xxd` on our quantized model and prints the output:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4-WhtGpvb-E",
        "colab_type": "code",
        "outputId": "f975721f-bdd1-440a-93af-55f13c4c8690",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 3808
        }
      },
      "source": [
        "# Install xxd if it is not available\n",
        "!apt-get -qq install xxd\n",
        "# Save the file as a C source file\n",
        "!xxd -i sine_model_quantized.tflite > sine_model_quantized.cc\n",
        "# Print the source file\n",
        "!cat sine_model_quantized.cc"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "unsigned char sine_model_quantized_tflite[] = {\n",
            "  0x18, 0x00, 0x00, 0x00, 0x54, 0x46, 0x4c, 0x33, 0x00, 0x00, 0x0e, 0x00,\n",
            "  0x18, 0x00, 0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x14, 0x00,\n",
            "  0x0e, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x10, 0x0a, 0x00, 0x00,\n",
            "  0xb8, 0x05, 0x00, 0x00, 0xa0, 0x05, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x0b, 0x00, 0x00, 0x00, 0x90, 0x05, 0x00, 0x00, 0x7c, 0x05, 0x00, 0x00,\n",
            "  0x24, 0x05, 0x00, 0x00, 0xd4, 0x04, 0x00, 0x00, 0xc4, 0x00, 0x00, 0x00,\n",
            "  0x74, 0x00, 0x00, 0x00, 0x24, 0x00, 0x00, 0x00, 0x1c, 0x00, 0x00, 0x00,\n",
            "  0x14, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x54, 0xf6, 0xff, 0xff, 0x58, 0xf6, 0xff, 0xff, 0x5c, 0xf6, 0xff, 0xff,\n",
            "  0x60, 0xf6, 0xff, 0xff, 0xc2, 0xfa, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x40, 0x00, 0x00, 0x00, 0x7c, 0x19, 0xa7, 0x3e, 0x99, 0x81, 0xb9, 0x3e,\n",
            "  0x56, 0x8b, 0x9f, 0x3e, 0x88, 0xd8, 0x12, 0xbf, 0x74, 0x10, 0x56, 0x3e,\n",
            "  0xfe, 0xc6, 0xdf, 0xbe, 0xf2, 0x10, 0x5a, 0xbe, 0xf0, 0xe2, 0x0a, 0xbe,\n",
            "  0x10, 0x5a, 0x98, 0xbe, 0xb9, 0x36, 0xce, 0x3d, 0x8f, 0x7f, 0x87, 0x3e,\n",
            "  0x2c, 0xb1, 0xfd, 0xbd, 0xe6, 0xa6, 0x8a, 0xbe, 0xa5, 0x3e, 0xda, 0x3e,\n",
            "  0x50, 0x34, 0xed, 0xbd, 0x90, 0x91, 0x69, 0xbe, 0x0e, 0xfb, 0xff, 0xff,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0x67, 0x41, 0x48, 0xbf,\n",
            "  0x24, 0xcd, 0xa0, 0xbe, 0xb7, 0x92, 0x0c, 0xbf, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x98, 0xfe, 0x3c, 0x3f, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x4a, 0x17, 0x9a, 0xbe,\n",
            "  0x41, 0xcb, 0xb6, 0xbe, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x13, 0xd6, 0x1e, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x5a, 0xfb, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00,\n",
            "  0x4b, 0x98, 0xdd, 0xbd, 0x40, 0x6b, 0xcb, 0xbe, 0x36, 0x0c, 0xd4, 0x3c,\n",
            "  0xbd, 0x44, 0xb5, 0x3e, 0x95, 0x70, 0xe3, 0x3e, 0xe7, 0xac, 0x86, 0x3e,\n",
            "  0x00, 0xc4, 0x4e, 0x3d, 0x7e, 0xa6, 0x1d, 0x3e, 0xbd, 0x87, 0xbb, 0x3e,\n",
            "  0xb4, 0xb8, 0x09, 0xbf, 0xa1, 0x1f, 0xf8, 0xbe, 0x8d, 0x90, 0xdd, 0x3e,\n",
            "  0xde, 0xfa, 0x6f, 0xbe, 0xb2, 0x75, 0xe4, 0x3d, 0x6e, 0xfe, 0x36, 0x3e,\n",
            "  0x20, 0x18, 0xc2, 0xbe, 0x39, 0xc7, 0xfb, 0xbe, 0xfe, 0xa4, 0x30, 0xbe,\n",
            "  0xf7, 0x91, 0xde, 0xbe, 0xde, 0xab, 0x24, 0x3e, 0xfb, 0xbb, 0xce, 0x3e,\n",
            "  0xeb, 0x23, 0x80, 0xbe, 0x7b, 0x58, 0x73, 0xbe, 0x9a, 0x2e, 0x03, 0x3e,\n",
            "  0x10, 0x42, 0xa9, 0xbc, 0x10, 0x12, 0x64, 0xbd, 0xe3, 0x8d, 0x0c, 0x3d,\n",
            "  0x9e, 0x48, 0x97, 0xbe, 0x34, 0x51, 0xd4, 0xbe, 0x02, 0x3b, 0x0d, 0x3e,\n",
            "  0x62, 0x67, 0x89, 0xbe, 0x74, 0xdf, 0xa2, 0x3d, 0xf3, 0x25, 0xb3, 0xbe,\n",
            "  0xef, 0x34, 0x7b, 0x3d, 0x61, 0x70, 0xe3, 0x3d, 0xba, 0x76, 0xc0, 0xbe,\n",
            "  0x7d, 0xe9, 0xa7, 0x3e, 0xc3, 0xab, 0xd0, 0xbe, 0xcf, 0x7c, 0xdb, 0xbe,\n",
            "  0x70, 0x27, 0x9a, 0xbe, 0x98, 0xf5, 0x3c, 0xbd, 0xff, 0x4b, 0x4b, 0x3e,\n",
            "  0x7e, 0xa0, 0xf8, 0xbd, 0xd4, 0x6e, 0x86, 0x3d, 0x00, 0x4a, 0x07, 0x3a,\n",
            "  0x4c, 0x24, 0x61, 0xbe, 0x54, 0x68, 0xf7, 0xbd, 0x02, 0x3f, 0x77, 0xbe,\n",
            "  0x23, 0x79, 0xb3, 0x3e, 0x1c, 0x83, 0xad, 0xbd, 0xc8, 0x92, 0x8d, 0x3e,\n",
            "  0xa8, 0xf3, 0x15, 0xbd, 0xe6, 0x4d, 0x6c, 0x3d, 0xac, 0xe7, 0x98, 0xbe,\n",
            "  0x81, 0xec, 0xbd, 0x3e, 0xe2, 0x55, 0x73, 0x3e, 0xc1, 0x77, 0xc7, 0x3e,\n",
            "  0x6e, 0x1b, 0x5e, 0x3d, 0x27, 0x78, 0x02, 0x3f, 0xd4, 0x21, 0x90, 0x3d,\n",
            "  0x52, 0xdc, 0x1f, 0x3e, 0xbf, 0xda, 0x88, 0x3e, 0x80, 0x79, 0xe3, 0xbd,\n",
            "  0x40, 0x6f, 0x10, 0xbe, 0x20, 0x43, 0x2e, 0xbd, 0xf0, 0x76, 0xc5, 0xbd,\n",
            "  0xcc, 0xa0, 0x04, 0xbe, 0xf0, 0x69, 0xd7, 0xbe, 0xb1, 0xfe, 0x64, 0xbe,\n",
            "  0x20, 0x41, 0x84, 0xbe, 0xb2, 0xc3, 0x26, 0xbe, 0xd8, 0xf4, 0x09, 0xbe,\n",
            "  0x64, 0x44, 0xd1, 0x3d, 0xd5, 0xe1, 0xc8, 0xbe, 0x35, 0xbc, 0x3f, 0xbe,\n",
            "  0xc0, 0x94, 0x82, 0x3d, 0xdc, 0x2b, 0xb1, 0xbd, 0x02, 0xdb, 0xbf, 0xbe,\n",
            "  0xa5, 0x7f, 0x8a, 0x3e, 0x21, 0xb4, 0xa2, 0x3e, 0xcd, 0x86, 0x56, 0xbf,\n",
            "  0x9c, 0x3b, 0x76, 0xbc, 0x85, 0x6d, 0x60, 0xbf, 0x86, 0x00, 0x3c, 0xbe,\n",
            "  0xc1, 0x23, 0x7e, 0x3e, 0x96, 0xcd, 0x3f, 0x3e, 0x86, 0x91, 0x2d, 0x3e,\n",
            "  0x55, 0xef, 0x87, 0x3e, 0x7e, 0x97, 0x03, 0xbe, 0x2a, 0xcd, 0x01, 0x3e,\n",
            "  0x32, 0xc9, 0x8e, 0xbe, 0x72, 0x77, 0x3b, 0xbe, 0xe0, 0xa1, 0xbc, 0xbe,\n",
            "  0x8d, 0xb7, 0xa7, 0x3e, 0x1c, 0x05, 0x95, 0xbe, 0xf7, 0x1f, 0xbb, 0x3e,\n",
            "  0xc9, 0x3e, 0xd6, 0x3e, 0x80, 0x42, 0xe9, 0xbd, 0x27, 0x0c, 0xd2, 0xbe,\n",
            "  0x5c, 0x32, 0x34, 0xbe, 0x14, 0xcb, 0xca, 0xbd, 0xdd, 0x3a, 0x67, 0xbe,\n",
            "  0x1c, 0xbb, 0x8d, 0xbe, 0x91, 0xac, 0x5c, 0xbe, 0x52, 0x40, 0x6f, 0xbe,\n",
            "  0xd7, 0x71, 0x94, 0x3e, 0x18, 0x71, 0x09, 0xbe, 0x9b, 0x29, 0xd9, 0xbe,\n",
            "  0x7d, 0x66, 0xd2, 0xbe, 0x98, 0xd6, 0xb2, 0xbe, 0x00, 0xc9, 0x84, 0x3a,\n",
            "  0xbc, 0xda, 0xc2, 0xbd, 0x1d, 0xc2, 0x1b, 0xbf, 0xd4, 0xdd, 0x92, 0x3e,\n",
            "  0x07, 0x87, 0x6c, 0xbe, 0x40, 0xc2, 0x3b, 0xbe, 0xbd, 0xe2, 0x9c, 0x3e,\n",
            "  0x0a, 0xb5, 0xa0, 0xbe, 0xe2, 0xd5, 0x9c, 0xbe, 0x3e, 0xbb, 0x7c, 0x3e,\n",
            "  0x17, 0xb4, 0xcf, 0x3e, 0xd5, 0x8e, 0xc8, 0xbe, 0x7c, 0xf9, 0x5c, 0x3e,\n",
            "  0x80, 0xfc, 0x0d, 0x3d, 0xc5, 0xd5, 0x8b, 0x3e, 0xf5, 0x17, 0xa2, 0x3e,\n",
            "  0xc7, 0x60, 0x89, 0xbe, 0xec, 0x95, 0x87, 0x3d, 0x7a, 0xc2, 0x5d, 0xbf,\n",
            "  0x77, 0x94, 0x98, 0x3e, 0x77, 0x39, 0x07, 0xbc, 0x42, 0x29, 0x00, 0x3e,\n",
            "  0xaf, 0xd0, 0xa9, 0x3e, 0x31, 0x23, 0xc4, 0xbe, 0x95, 0x36, 0x5b, 0xbe,\n",
            "  0xc7, 0xdc, 0x83, 0xbe, 0x1e, 0x6b, 0x47, 0x3e, 0x5b, 0x24, 0x99, 0x3e,\n",
            "  0x99, 0x27, 0x54, 0x3e, 0xc8, 0x20, 0xdd, 0xbd, 0x5a, 0x86, 0x2f, 0x3e,\n",
            "  0x80, 0xf0, 0x69, 0xbe, 0x44, 0xfc, 0x84, 0xbd, 0x82, 0xa0, 0x2a, 0xbe,\n",
            "  0x87, 0xe6, 0x2a, 0x3e, 0xd8, 0x34, 0xae, 0x3d, 0x50, 0xbd, 0xb5, 0x3e,\n",
            "  0xc4, 0x8c, 0x88, 0xbe, 0xe3, 0xbc, 0xa5, 0x3e, 0xa9, 0xda, 0x9e, 0x3e,\n",
            "  0x3e, 0xb8, 0x23, 0xbe, 0x80, 0x90, 0x15, 0x3d, 0x97, 0x3f, 0xc3, 0x3e,\n",
            "  0xca, 0x5c, 0x9d, 0x3e, 0x21, 0xe8, 0xe1, 0x3e, 0xc0, 0x49, 0x01, 0xbc,\n",
            "  0x00, 0x0b, 0x88, 0xbd, 0x3f, 0xf7, 0xca, 0x3c, 0xfb, 0x5a, 0xb1, 0x3e,\n",
            "  0x60, 0xd2, 0x0d, 0x3c, 0xce, 0x23, 0x78, 0xbf, 0x8f, 0x4f, 0xb9, 0xbe,\n",
            "  0x69, 0x6a, 0x34, 0xbf, 0x4b, 0x5e, 0xa9, 0x3e, 0x64, 0x8c, 0xd9, 0x3e,\n",
            "  0x52, 0x77, 0x36, 0x3e, 0xeb, 0xaf, 0xbe, 0x3e, 0x40, 0xbe, 0x36, 0x3c,\n",
            "  0x08, 0x65, 0x3b, 0xbd, 0x55, 0xe0, 0x66, 0xbd, 0xd2, 0xe8, 0x9b, 0xbe,\n",
            "  0x86, 0xe3, 0x09, 0xbe, 0x93, 0x3d, 0xdd, 0x3e, 0x0f, 0x66, 0x18, 0x3f,\n",
            "  0x18, 0x05, 0x33, 0xbd, 0xde, 0x15, 0xd7, 0xbe, 0xaa, 0xcf, 0x49, 0xbe,\n",
            "  0xa2, 0xa5, 0x64, 0x3e, 0xe6, 0x9c, 0x42, 0xbe, 0x54, 0x42, 0xcc, 0x3d,\n",
            "  0xa0, 0xbd, 0x9d, 0xbe, 0xc2, 0x69, 0x48, 0x3e, 0x5b, 0x8b, 0xa2, 0xbe,\n",
            "  0xc0, 0x13, 0x87, 0x3d, 0x36, 0xfd, 0x69, 0x3e, 0x05, 0x86, 0x40, 0xbe,\n",
            "  0x1e, 0x7a, 0xce, 0xbe, 0x46, 0x13, 0xa7, 0xbe, 0x68, 0x52, 0x86, 0xbe,\n",
            "  0x04, 0x9e, 0x86, 0xbd, 0x8c, 0x54, 0xc1, 0x3d, 0xe0, 0x3b, 0xad, 0x3c,\n",
            "  0x42, 0x67, 0x85, 0xbd, 0xea, 0x97, 0x42, 0x3e, 0x6e, 0x13, 0x3b, 0xbf,\n",
            "  0x56, 0x5b, 0x16, 0x3e, 0xaa, 0xab, 0xdf, 0x3e, 0xc8, 0x41, 0x36, 0x3d,\n",
            "  0x24, 0x2d, 0x47, 0xbe, 0x77, 0xa5, 0xae, 0x3e, 0xc0, 0xc2, 0x5b, 0x3c,\n",
            "  0xac, 0xac, 0x4e, 0x3e, 0x99, 0xec, 0x13, 0xbe, 0xf2, 0xab, 0x73, 0x3e,\n",
            "  0xaa, 0xa1, 0x48, 0xbe, 0xe8, 0xd3, 0x01, 0xbe, 0x60, 0xb7, 0xc7, 0xbd,\n",
            "  0x64, 0x72, 0xd3, 0x3d, 0x83, 0xd3, 0x99, 0x3e, 0x0c, 0x76, 0x34, 0xbe,\n",
            "  0x42, 0xda, 0x0d, 0x3e, 0xfb, 0x47, 0x9a, 0x3e, 0x8b, 0xdc, 0x92, 0xbe,\n",
            "  0x56, 0x7f, 0x6b, 0x3e, 0x04, 0xd4, 0x88, 0xbd, 0x11, 0x9e, 0x80, 0x3e,\n",
            "  0x3c, 0x89, 0xff, 0x3d, 0xb3, 0x3e, 0x88, 0x3e, 0xf7, 0xf0, 0x88, 0x3e,\n",
            "  0x28, 0xfb, 0xc9, 0xbe, 0x53, 0x3e, 0xcf, 0x3e, 0xac, 0x75, 0xdc, 0xbe,\n",
            "  0xdd, 0xca, 0xd7, 0x3e, 0x01, 0x58, 0xa7, 0x3e, 0x29, 0xb8, 0x13, 0xbf,\n",
            "  0x76, 0x81, 0x12, 0xbc, 0x28, 0x8b, 0x16, 0xbf, 0x0e, 0xec, 0x0e, 0x3e,\n",
            "  0x40, 0x0a, 0xdb, 0xbd, 0x98, 0xec, 0xbf, 0xbd, 0x32, 0x55, 0x0c, 0xbe,\n",
            "  0xfb, 0xf9, 0xc9, 0x3e, 0x83, 0x4a, 0x6d, 0xbe, 0x76, 0x59, 0xe2, 0xbe,\n",
            "  0x54, 0x7d, 0x9f, 0xbb, 0x9d, 0xe8, 0x95, 0x3e, 0x5c, 0xd3, 0xd0, 0x3d,\n",
            "  0x19, 0x8a, 0xb0, 0x3e, 0xde, 0x6f, 0x2e, 0xbe, 0xd0, 0x16, 0x83, 0x3d,\n",
            "  0x9c, 0x7d, 0x11, 0xbf, 0x2b, 0xcc, 0x25, 0x3c, 0x2a, 0xa5, 0x27, 0xbe,\n",
            "  0x22, 0x14, 0xc7, 0xbe, 0x5e, 0x7a, 0xac, 0x3e, 0x4e, 0x41, 0x94, 0xbe,\n",
            "  0x5a, 0x68, 0x7b, 0x3e, 0x86, 0xfd, 0x4e, 0x3e, 0xa2, 0x56, 0x6a, 0xbe,\n",
            "  0xca, 0xfe, 0x81, 0xbe, 0x43, 0xc3, 0xb1, 0xbd, 0xc5, 0xb8, 0xa7, 0x3e,\n",
            "  0x55, 0x23, 0xcd, 0x3e, 0xaf, 0x2e, 0x76, 0x3e, 0x69, 0xa8, 0x90, 0xbe,\n",
            "  0x0d, 0xba, 0xb9, 0x3e, 0x66, 0xff, 0xff, 0xff, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x40, 0x00, 0x00, 0x00, 0x53, 0xd6, 0xe2, 0x3d, 0x66, 0xb6, 0xcc, 0x3e,\n",
            "  0x03, 0xe7, 0xf6, 0x3e, 0xe0, 0x28, 0x10, 0xbf, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x3e, 0x3d, 0xb0, 0x3e, 0x00, 0x00, 0x00, 0x00, 0x62, 0xf0, 0x77, 0x3e,\n",
            "  0xa6, 0x9d, 0xa4, 0x3e, 0x3a, 0x4b, 0xf3, 0xbe, 0x71, 0x9e, 0xa7, 0x3e,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x34, 0x39, 0xa2, 0x3e, 0x00, 0x00, 0x00, 0x00,\n",
            "  0xcc, 0x9c, 0x4a, 0x3e, 0xab, 0x40, 0xa3, 0x3e, 0xb2, 0xff, 0xff, 0xff,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x40, 0x00, 0x00, 0x00, 0xb3, 0x71, 0x67, 0x3f,\n",
            "  0x9a, 0x7a, 0x95, 0xbf, 0xe1, 0x48, 0xe8, 0xbe, 0x8a, 0x72, 0x96, 0x3e,\n",
            "  0x00, 0xd2, 0xd3, 0xbb, 0x1a, 0xc5, 0xd7, 0x3f, 0xac, 0x7e, 0xc8, 0xbe,\n",
            "  0x90, 0xa7, 0x95, 0xbe, 0x3b, 0xd7, 0xdc, 0xbe, 0x41, 0xa8, 0x16, 0x3f,\n",
            "  0x50, 0x5b, 0xcb, 0x3f, 0x52, 0xb9, 0xed, 0xbe, 0x2e, 0xa7, 0xc6, 0xbe,\n",
            "  0xaf, 0x0f, 0x14, 0xbf, 0xb3, 0xda, 0x59, 0x3f, 0x02, 0xec, 0xd7, 0xbe,\n",
            "  0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x04, 0x00, 0x06, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x66, 0x11, 0x1f, 0xbf,\n",
            "  0xb8, 0xfb, 0xff, 0xff, 0x0f, 0x00, 0x00, 0x00, 0x54, 0x4f, 0x43, 0x4f,\n",
            "  0x20, 0x43, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x65, 0x64, 0x2e, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x14, 0x00,\n",
            "  0x04, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0xf0, 0x00, 0x00, 0x00, 0xe4, 0x00, 0x00, 0x00, 0xd8, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x90, 0x00, 0x00, 0x00,\n",
            "  0x48, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xce, 0xff, 0xff, 0xff,\n",
            "  0x00, 0x00, 0x00, 0x08, 0x18, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x1c, 0xfc, 0xff, 0xff, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,\n",
            "  0x08, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00,\n",
            "  0x14, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x10, 0x00,\n",
            "  0x0e, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x08, 0x1c, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xba, 0xff, 0xff, 0xff,\n",
            "  0x00, 0x00, 0x00, 0x01, 0x01, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00,\n",
            "  0x03, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,\n",
            "  0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x16, 0x00, 0x00, 0x00,\n",
            "  0x08, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x10, 0x00, 0x0e, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x08, 0x24, 0x00, 0x00, 0x00, 0x18, 0x00, 0x00, 0x00,\n",
            "  0x0c, 0x00, 0x00, 0x00, 0x00, 0x00, 0x06, 0x00, 0x08, 0x00, 0x07, 0x00,\n",
            "  0x06, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x03, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x0a, 0x00, 0x00, 0x00, 0x10, 0x03, 0x00, 0x00, 0xa4, 0x02, 0x00, 0x00,\n",
            "  0x40, 0x02, 0x00, 0x00, 0xf4, 0x01, 0x00, 0x00, 0xac, 0x01, 0x00, 0x00,\n",
            "  0x48, 0x01, 0x00, 0x00, 0xfc, 0x00, 0x00, 0x00, 0xb4, 0x00, 0x00, 0x00,\n",
            "  0x50, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x26, 0xfd, 0xff, 0xff,\n",
            "  0x3c, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x18, 0xfd, 0xff, 0xff, 0x20, 0x00, 0x00, 0x00,\n",
            "  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31,\n",
            "  0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x34, 0x2f, 0x4d, 0x61, 0x74,\n",
            "  0x4d, 0x75, 0x6c, 0x5f, 0x62, 0x69, 0x61, 0x73, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x6e, 0xfd, 0xff, 0xff,\n",
            "  0x50, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x60, 0xfd, 0xff, 0xff, 0x34, 0x00, 0x00, 0x00,\n",
            "  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31,\n",
            "  0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x34, 0x2f, 0x4d, 0x61, 0x74,\n",
            "  0x4d, 0x75, 0x6c, 0x2f, 0x52, 0x65, 0x61, 0x64, 0x56, 0x61, 0x72, 0x69,\n",
            "  0x61, 0x62, 0x6c, 0x65, 0x4f, 0x70, 0x2f, 0x74, 0x72, 0x61, 0x6e, 0x73,\n",
            "  0x70, 0x6f, 0x73, 0x65, 0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0xce, 0xfd, 0xff, 0xff,\n",
            "  0x34, 0x00, 0x00, 0x00, 0x08, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0xc0, 0xfd, 0xff, 0xff, 0x19, 0x00, 0x00, 0x00,\n",
            "  0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31,\n",
            "  0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x33, 0x2f, 0x52, 0x65, 0x6c,\n",
            "  0x75, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x12, 0xfe, 0xff, 0xff, 0x3c, 0x00, 0x00, 0x00,\n",
            "  0x03, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x04, 0xfe, 0xff, 0xff, 0x20, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75,\n",
            "  0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e,\n",
            "  0x73, 0x65, 0x5f, 0x33, 0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c, 0x5f,\n",
            "  0x62, 0x69, 0x61, 0x73, 0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0x5a, 0xfe, 0xff, 0xff, 0x50, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x4c, 0xfe, 0xff, 0xff, 0x34, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75,\n",
            "  0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e,\n",
            "  0x73, 0x65, 0x5f, 0x33, 0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c, 0x2f,\n",
            "  0x52, 0x65, 0x61, 0x64, 0x56, 0x61, 0x72, 0x69, 0x61, 0x62, 0x6c, 0x65,\n",
            "  0x4f, 0x70, 0x2f, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x70, 0x6f, 0x73, 0x65,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x10, 0x00, 0x00, 0x00, 0xba, 0xfe, 0xff, 0xff, 0x34, 0x00, 0x00, 0x00,\n",
            "  0x0a, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0xac, 0xfe, 0xff, 0xff, 0x19, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75,\n",
            "  0x65, 0x6e, 0x74, 0x69, 0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e,\n",
            "  0x73, 0x65, 0x5f, 0x32, 0x2f, 0x52, 0x65, 0x6c, 0x75, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0xfe, 0xfe, 0xff, 0xff, 0x3c, 0x00, 0x00, 0x00, 0x05, 0x00, 0x00, 0x00,\n",
            "  0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0xf0, 0xfe, 0xff, 0xff,\n",
            "  0x20, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69,\n",
            "  0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32,\n",
            "  0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c, 0x5f, 0x62, 0x69, 0x61, 0x73,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x46, 0xff, 0xff, 0xff, 0x50, 0x00, 0x00, 0x00, 0x06, 0x00, 0x00, 0x00,\n",
            "  0x0c, 0x00, 0x00, 0x00, 0x04, 0x00, 0x00, 0x00, 0x38, 0xff, 0xff, 0xff,\n",
            "  0x34, 0x00, 0x00, 0x00, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x74, 0x69,\n",
            "  0x61, 0x6c, 0x5f, 0x31, 0x2f, 0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32,\n",
            "  0x2f, 0x4d, 0x61, 0x74, 0x4d, 0x75, 0x6c, 0x2f, 0x52, 0x65, 0x61, 0x64,\n",
            "  0x56, 0x61, 0x72, 0x69, 0x61, 0x62, 0x6c, 0x65, 0x4f, 0x70, 0x2f, 0x74,\n",
            "  0x72, 0x61, 0x6e, 0x73, 0x70, 0x6f, 0x73, 0x65, 0x00, 0x00, 0x00, 0x00,\n",
            "  0x02, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0xa6, 0xff, 0xff, 0xff, 0x48, 0x00, 0x00, 0x00, 0x09, 0x00, 0x00, 0x00,\n",
            "  0x2c, 0x00, 0x00, 0x00, 0x0c, 0x00, 0x00, 0x00, 0x08, 0x00, 0x0c, 0x00,\n",
            "  0x04, 0x00, 0x08, 0x00, 0x08, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x04, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x7f, 0x43,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0d, 0x00, 0x00, 0x00,\n",
            "  0x64, 0x65, 0x6e, 0x73, 0x65, 0x5f, 0x32, 0x5f, 0x69, 0x6e, 0x70, 0x75,\n",
            "  0x74, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x00, 0x00, 0x0e, 0x00, 0x14, 0x00, 0x04, 0x00,\n",
            "  0x00, 0x00, 0x08, 0x00, 0x0c, 0x00, 0x10, 0x00, 0x0e, 0x00, 0x00, 0x00,\n",
            "  0x28, 0x00, 0x00, 0x00, 0x07, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x08, 0x00, 0x00, 0x00, 0x04, 0x00, 0x04, 0x00, 0x04, 0x00, 0x00, 0x00,\n",
            "  0x08, 0x00, 0x00, 0x00, 0x49, 0x64, 0x65, 0x6e, 0x74, 0x69, 0x74, 0x79,\n",
            "  0x00, 0x00, 0x00, 0x00, 0x02, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00,\n",
            "  0x01, 0x00, 0x00, 0x00, 0x01, 0x00, 0x00, 0x00, 0x10, 0x00, 0x00, 0x00,\n",
            "  0x00, 0x00, 0x0a, 0x00, 0x0c, 0x00, 0x07, 0x00, 0x00, 0x00, 0x08, 0x00,\n",
            "  0x0a, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x09, 0x03, 0x00, 0x00, 0x00\n",
            "};\n",
            "unsigned int sine_model_quantized_tflite_len = 2640;\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1sqrhBLXwILt",
        "colab_type": "text"
      },
      "source": [
        "We can either copy and paste this output into our project's source code, or download the file using the collapsible menu on the left hand side of this Colab.\n",
        "\n"
      ]
    }
  ]
}